A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science, (244):113274, Elsevier, 2024. [PUMA: topic_engineering area_architectures FRP using experimental deformation direct learning application identification world parameter machine Real results highly non-linear behavior]
Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, and Zilong Wu (Eds.), Intelligent Nanotechnology, 113--144, Elsevier, 2023. [PUMA: topic_physchemistry Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement] URL
Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] URL
NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: topic_federatedlearn Design Machine Modeling, Near-data Prediction, exploration learning, processing, space] URL
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: topic_lifescience AI, Artificial CNN, Communications DICOM, Diagnosis; Digital HCC, Imaging Individual ML, MVI, Medicine; NAFLD, NASH, Prognosis Reporting TACE, TRIPOD, Transparent WSIs, a and artificial carcinoma; chemoembolisation; convolutional data deep diagnostic disease; fatty for hepatocellular images; imaging; in integration intelligence; invasion; learning; liver machine microvascular model multimodal multivariable network; neural non-alcoholic of or prediction slide steatohepatitis; support system; transarterial whole]
Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, December 2021. [PUMA: classification coding extraction; feature learning lncRNA; machine problems; sequence;]
Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning. Evol. Comput., (27)1:99--127, MIT Press, 2019. [PUMA: Automated algorithm analysis; black-box continuous exploratory landscape learning; machine optimization. optimization; selection; single-objective]
Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: Automated algorithm analysis; approaches; automated combinatorial configuration; continuous data exploratory feature-based landscape learning; machine metalearning optimisation; selection; streams.;]
Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study. J. Cachexia Sarcopenia Muscle, (12)4:1011--1023, Wiley, August 2021. [PUMA: Appendicular Body Fat Machine Muscle Proteomics fat index; learning; mass mass; muscle skeletal]
Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am. J. Hum. Genet., (108)10:1946--1963, Elsevier BV, October 2021. [PUMA: Diseases Network; UDN; Undiagnosed clinical digenic disease disease; learning; machine oligogenic prediction; rare topic_lifescience]
Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, March 2021. [PUMA: (SHM) composite composites; connected convolutional dense fully health learning; machine monitoring networks; neural rotors; structural]
Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J. Med. Imaging (Bellingham), (10)4:044502, SPIE-Intl Soc Optical Eng, July 2023. [PUMA: topic_visualcomputing graph imaging; learning; machine medical multi-modal networks; neural prediction stroke]
Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 36–46, Association for Computing Machinery, New York, NY, USA, 2024. [PUMA: topic_visualcomputing XAI, explainability, learning, machine trust] URL
Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. NeuroImage Clin., (37)103320:103320, Elsevier BV, January 2023. [PUMA: topic_neuroinspired Dementia; Diagnosis; MRI; Machine Neurodegeneration; Volumetry learning; topic_lifescience unit_test]
CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation. Genome Med., (15)1:61, August 2023. [PUMA: topic_lifescience Cell Immunocompromised; Machine Macrophages; Myocardial Single-cell infarction; learning; therapy;]
Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers (Basel), (15)7April 2023. [PUMA: topic_lifescience cancer cancer; classification; colorectal convolutional filter; hyperspectral imaging; learning; machine median networks; post-processing; pre-processing]
MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)December 2022. [PUMA: topic_federatedlearn Python classification evaluation evaluation; learning; machine model package; problems; score unified]
Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: topic_federatedlearn AutoML; analysis; classification data learning; machine problems; visualization]